Barriers to prosperity: the harmful impact of entry regulations on income inequality


Entry regulations, including fees, permits and licenses, can make it prohibitively difficult for low-income individuals to establish footholds in many industries, even at the entry-level. As such, these regulations increase income inequality by either preventing access to higher paying professions or imposing costs on individuals choosing to enter illegally and provide unlicensed services. To estimate this relationship empirically, we combine entry regulations data from the World Bank’s Doing Business Index with various measures of income inequality, including Gini coefficients and income shares to form a panel of 115 countries. We find that countries with more stringent entry regulations tend to experience more income inequality. In countries with average inequality, increasing the number of procedures required to start a new business by one standard deviation is associated with a 7.2% increase in the share of income accruing to the top decile of earners, and a 12.9% increase in the overall Gini coefficient. This result is robust to the measure of inequality, startup regulations, and potential endogeneity. We conclude by offering several policy recommendations designed to minimize the adverse effects of entry regulations.

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  1. 1.

    For example, to obtain a license to offer hair-braiding services legally in Pennsylvania, a person must train for 300 h at a licensed school, have a minimum of a tenth-grade education, and pass both a theory and a practical exam (McLaughlin 2013).

  2. 2.

    While the impact of government regulations on the size of the shadow economy and overall income inequality (including income earned in both the formal and informal sectors) is a very interesting topic, data limitations prevent further exploration. The best available data (see Dreher et al. 2014; Schneider 2005) covers the very limited time span of 1999–2003.

  3. 3.

    For a literature review of the early empirical research surrounding the relationship between occupational licensing and quality, see Gross (1986).

  4. 4.

    McLaughlin et al. (2014) review three studies that observe a positive relationship between licensing and service quality. Feldman and Begun (1985) find that occupational restrictions in optometry improve the quality of eye examinations, Martin (1982) discovers a positive correlation between reciprocal licensing and quality, and Holen (1978) finds that entry requirements for dentists are associated with a lower rate of dental-health neglect. The authors reviewed 13 studies that observe a neutral or negative relationship between licensing and quality, including Carroll and Gaston’s (1981) study of electricians.

  5. 5.

    Stigler (1970) provides several examples of regressive public policy, including the provision of higher education, fire and police services, farm policy, minimum wage rules, Social Security, public housing and tax exemptions for charitable contributions.

  6. 6.

    The database can be accessed at

  7. 7.

    The reports and datasets are available from the Fraser Institute at

  8. 8.

    The KOF Index of Globalization measures globalization along three dimensions (economic, social, and political). The data and a description of the methodology used in its construction are available at

  9. 9.

    The WDI income share data are available at

  10. 10.

    Despite the care taken to produce a comparable income share series, the data are not perfectly compatible across countries, as nations use different survey designs and measure different welfare concepts (i.e., income versus expenditures). As there is no systemic relationship between income and expenditure shares, we follow Lakner and Milanovic (2013) and make no adjustment for this difference.

  11. 11.

    The Gini coefficient is equivalent to twice the area between the Lorenz curve and the line of perfect equality.

  12. 12.

    The ATG dataset is available at

  13. 13.

    The Doing Business database is available at

  14. 14.

    The Barro-Lee dataset is available at:

  15. 15.

    The data are from the World Development Indicators database at

  16. 16.

    The Penn World 9.0 dataset is available at:

  17. 17.

    The observations on ethnic fractionalization are reported for the early 1900 s only, so we employ those measures under the assumption that ethnic fractionalization is slow to change.

  18. 18.

    Because fewer than three observations are available on so many nations in our unbalanced panel, we cannot estimate model Eq. (1) with country-level (cross-sectional) fixed effects (\(\alpha_{i} )\), as doing so would effectively dummy-out most of the variation in the dependent variable.

  19. 19.

    To ensure that this result is not idiosyncratic to Freedom House’s measure of democracy, we also re-estimate columns 7 and 8 from Table 3 using an alternative measure of democracy, the Polity IV (2016) panel, which reflects six institutional/governmental qualities, such as political competition and executive authority, for up to 167 countries between 1800 and 2016. The index ranges in value from − 10 (monarchy) to + 10 (consolidated democracy). The estimation results are virtually identical across all of the models’ covariates, with positive and statistically significant coefficients on the polity measure of democracy. Like the Freedom House measure, these results suggest that more authoritarian nations exhibit less income inequality, all else equal. The Polity-based estimates are not reported, but are available from the authors upon request.

  20. 20.

    The average coefficient on Log GDP is 32.3841 and the average coefficient on Log GDP squared is − 1.8246. The turning point of the parabola thus is − 32.3841/(2 × (− 1.8246)) = 8.8743 or $7146 (Exp[8.8743]).

  21. 21.

    Under the null hypothesis that the instruments are valid, the test for over-identifying restrictions regresses the residuals from the TSLS regression on the full set of instruments. Multiplying the goodness of fit (R2) by the sample size (N) yields a test statistic that is asymptotically χ2 distributed under the null hypothesis, where the degrees of freedom equal the instrument rank less the number of endogenous variables.

  22. 22.

    Including all of the regulatory instruments as control variables would violate the rank condition, so we must exclude at least one regulatory instrument.

  23. 23.

    The over-identification test statistic (with 24 degrees of freedom) equals 12.49, well below the 10% critical value of 33.20.

  24. 24.

    The over-identification test statistic (with 27 degrees of freedom) equals 21.19, well below the 10% critical value of 36.70.

  25. 25.

    The over-identification test statistic (with 24 degrees of freedom) equals 22.94, well below the 10% critical value of 33.20.

  26. 26.

    In fact, this is supposed to be the first step undertaken by a regulatory agency when performing an economic analysis of a proposed rule. See Ellig and McLaughlin (2012) and Ellig et al. (2013).


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The authors thank Diana Thomas, Stefanie Haeffele-Balch, Ted Bolema, Bill Shughart, Richard Williams, and three anonymous reviewers for helpful comments on early drafts. All mistakes are the authors’ responsibility.

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Correspondence to Dustin Chambers.

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Chambers, D., McLaughlin, P.A. & Stanley, L. Barriers to prosperity: the harmful impact of entry regulations on income inequality. Public Choice 180, 165–190 (2019).

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  • Income inequality
  • Regulation
  • Entry regulations
  • Doing business
  • Gini coefficient

JEL Classification

  • D31
  • J38
  • K20